Recursive identification for multi-input–multi-output Hammerstein–Wiener system

References

• Bai, E.-W. (1998). An optimal two-stage identification algorithm for Hammerstein–Wiener nonlinear systems. Automatica , 34 (3), 333–338.
   Web of Science ®Google Scholar
• Bai, E.-W. (2002). A blind approach to the Hammerstein–Wiener model identification. Automatica , 38 (6), 967–979.
   Web of Science ®Google Scholar
• Bai, E.-W. , & Fu, M. (2002). A blind approach to Hammerstein model identification. IEEE Transactions on Signal Processing , 50 (7), 1610–1619.
   Web of Science ®Google Scholar
• Bai, E.-W. , & Li, D. (2004). Convergence of the iterative Hammerstein system identification algorithm. IEEE Transactions on Automatic Control , 49 (11), 1929–1940.
   Web of Science ®Google Scholar
• Bai, J. , Mao, Z. , Yu, F. , & Wang, Y. (2014). Identification of MIMO Hammerstein–Wiener system. The 26th Chinese Control and Decision Conference (pp. 1186-1190). IEEE.
   Google Scholar
• Billings, S. A. , & Fakhouri, S. Y. (1978). Identification of a class of nonlinear systems using correlation analysis. Proceedings of the Institution of Electrical Engineers , 125 (7), 691–697.
   Google Scholar
• Boutayeb, M. , Aubry, D. , & Darouach, M. (1996). A robust and recursive identification method for MISO Hammerstein model. In Proceedings of Control'96, UKACC International Conference on Control (pp. 234–239). IET.
   Google Scholar
• Chen, X.-M. , & Chen, H.-F. (2011). Recursive identification for MIMO Hammerstein systems. IEEE Transactions on Automatic Control , 56 (4), 895–902.
   Web of Science ®Google Scholar
• Ding, F. , & Chen, T. (2005). Identification of Hammerstein nonlinear ARMAX systems. Automatica , 41 (9), 1479–1489.
   Web of Science ®Google Scholar
• Ding, F. , Shi, Y. , & Chen, T. (2007). Auxiliary model-based least-squares identification methods for Hammerstein output-error systems. Systems & Control Letters , 56 (5), 373–380.
   Web of Science ®Google Scholar
• Enqvist, M. (2010). Identification of block-oriented systems using the invariance property In Giri, F., & Bai, E.-W. (Eds),Lecture Notes in Control & Information Sciences 404 (pp. 147-158). London: Springer.
   Google Scholar
• Giri, F. , & Bai, E.-W. (2010). Block-oriented nonlinear system identification . London: Springer.
   Google Scholar
• Golub, G. H. , & Van Loan, C. F. (1996). Matrix Computations.(3rd ed.). Baltimore and London: The Johns Hipkins University Press.
   Google Scholar
• Golub, G. , & Pereyra, V. (2003). Separable nonlinear least squares: The variable projection method and its applications. Inverse Problems , 19 (2), R1–R26.
   Web of Science ®Google Scholar
• Goodwin, G. , & Sin, K. (1984). Adaptive filtering prediction and control. Englewood Cliffs, NJ: Prentice-Hall.
   Google Scholar
• Greblicki, W. (1992). Nonparametric identification of Wiener systems. IEEE Transactions on Information Theory , 38 (5), 1487–1493.
   Web of Science ®Google Scholar
• Greblicki, W. (2000). Continuous-time Hammerstein system identification. IEEE Transactions on Automatic Control , 45 (6), 1232–1236.
   Web of Science ®Google Scholar
• Harnischmacher, G. , & Marquardt, W. (2007). Nonlinear model predictive control of multivariable processes using block-structured models. Control Engineering Practice , 15 (10), 1238–1256.
   Web of Science ®Google Scholar
• Janczak, A. (2007). Instrumental variables approach to identification of a class of MIMO Wiener systems. Nonlinear Dynamics , 48 (3), 275–284.
   Web of Science ®Google Scholar
• Kalafatis, A. , Arifin, N. , Wang, L. , & Cluett, W. (1995). A new approach to the identification of pH processes based on the Wiener model. Chemical Engineering Science , 50 (23), 3693–3701.
   Web of Science ®Google Scholar
• Korenberg, M. J. , & Hunter, I. W. (1999). Two methods for identifying Wiener cascades having noninvertible static nonlinearities. Annals of Biomedical Engineering , 27 (6), 793–804.
   PubMed Web of Science ®Google Scholar
• Li, Y. , Mao, Z.-Z. , Wang, Y. , Yuan, P. , & Jia, M.-X. (2010). Identification of Hammerstein–Wiener models based on bias compensation recursive least squares. Acta Automatica Sinica , 36 (1), 163–168.
   Google Scholar
• Liu, Y. , & Ding, F. (2013). Convergence properties of the least squares estimation algorithm for multivariable systems. Applied Mathematical Modelling , 37 (1), 476–483.
   Web of Science ®Google Scholar
• Maksym, G. N. , Kearney, R. E. , & Bates, J. H. (1998). Nonparametric block-structured modeling of lung tissue strip mechanics. Annals of Biomedical Engineering , 26 (2), 242–252.
   PubMed Web of Science ®Google Scholar
• Mu, B.-Q. , & Chen, H.-F. (2013). Recursive identification of MIMO Wiener systems. IEEE Transactions on Automatic Control , 58 (3), 802–808.
   Web of Science ®Google Scholar
• Pajunen, G. (1992). Adaptive control of Wiener type nonlinear systems. Automatica , 28 (4), 781–785.
   Web of Science ®Google Scholar
• Rojas, C. R. , Barenthin, M. B. , Welsh, J. S. , & Hjalmarsson, H. (2010). The cost of complexity in system identification: frequency function estimation of finite impulse response systems. IEEE Transactions on Automatic Control , 55 (55), 2298–2309.
   Web of Science ®Google Scholar
• Schoukens, M. , Marconato, A. , Pintelon, R. , Vandersteen, G. , & Rolain, Y. (2015). Parametric identification of parallel Wiener–Hammerstein systems. Automatica , 51 , 111–122.
   Web of Science ®Google Scholar
• Schoukens, M. , Rolain, Y. , & Bai, E. W. (2012). Identification of Hammerstein–Wiener systems. Paper presented at the IFAC Symposium on System Identification.
   Google Scholar
• Sjöberg, J. , Zhang, Q. , Ljung, L. , Benveniste, A. , Delyon, B. , Glorennec, P.-Y. , … Juditsky, A. (1995). Nonlinear black-box modeling in system identification: A unified overview. Automatica , 31 (12), 1691–1724.
   Web of Science ®Google Scholar
• Wang, Y. (2009). Study and application of electric arc model for AC electric arc furnace ( Doctor dissertation). Shenyang, China: Northeastern University.
   Google Scholar
• Westwick, D. , & Verhaegen, M. (1996). Identifying MIMO Wiener systems using subspace model identification methods. Signal Processing , 52 (2), 235–258.
   Web of Science ®Google Scholar
• Westwick, D. T. , & Kearney, R. E. (2001). Separable least squares identification of nonlinear Hammerstein models: Application to stretch reflex dynamics. Annals of Biomedical Engineering , 29 (8), 707–718.
   PubMed Web of Science ®Google Scholar
• Wills, A. , Schön, T. B. , Ljung, L. , & Ninness, B. (2013). Identification of Hammerstein–Wiener models. Automatica , 49 (1), 70–81.
   Web of Science ®Google Scholar
• Yu, F. , Mao, Z. , & Jia, M. (2013). Recursive identification for Hammerstein–Wiener systems with dead-zone input nonlinearity. Journal of Process Control , 23 (8), 1108–1115.
   Web of Science ®Google Scholar
• Zhang, B. , & Mao, Z. Z. (2015). Consistent parameter estimation and convergence properties analysis of Hammerstein output-error models. International Journal of Control Automation & Systems , 13 (2), 302–310.
   Web of Science ®Google Scholar
• Zhu, Y. (2002). Estimation of an N–L–N Hammerstein–Wiener model. Automatica , 38 (9), 1607–1614.
   Web of Science ®Google Scholar